Note that this is a time-consuming step. The output “comp” structure resembles the input raw data structure, i.e. it contains a time course for each component and each trial. Furthermore, it contains the spatial mixing matrix. In principle you can continue analyzing the data on the component level by doing

cfg = [];
cfg = ...
freq = ft_freqanalysis(cfg, comp);

or

cfg = [];
cfg = ...
timelock = ft_timelockanalysis(cfg, comp);

but for this example we want to analyze the data eventually on the original channel level and only remove the components that represent the artifacts.

Make sure to plot and inspect all components. Write down the components that contain the eye artifacts. Very important is to know that on subsequent evaluations of the component decomposition result in components that can have a different order. That means that component numbers that you write down do not apply to another run of the ICA decomposition on the same data.

The spatial topography of the components aids in interpreting whether a component represents activity from the cortex, or non-cortical physiological activity (muscle, eyes, heart) or even non-physiological activity (line noise and other environmental noise). If you are trained in this type of analysis, you can relatively easily spot the components that represent the eye movements: 9, 14 and 10.

Besides the spatial topography you should inspect the time course of the components, which gives additional information on separating the cortical from the non-cortical contributions to the data.